To relax the constraints on supervision, CMR [1]} reconstructs category-specific textured mesh by training with a collection of monocular images and associated 2D supervisions, , 2D key-points, camera poses, and silhouette masks.
Thereafter, several follow-up studies further relax the supervision, , masks only, and improves the reconstruction results by exploiting different forms of prior.
Specifically, they incorporate the prior by enforcing various types of cycle consistencies, such as texture cycle consistency [2]}, [3]}, rotation adversarial cycle consistency [3]}, and interpolated consistency [5]}. Some of these methods also leverage external information, , category-level mesh templates [6]}, [3]}, and semantic parts provided by an external SCOPS model [2]}.
In parallel, Shelf-Sup [9]} first gives a coarse volumetric prediction, and then converts the coarse volume into a mesh followed by test-time optimization. Without categorical mesh templates in existing approaches, this design demonstrates its scalability to categories with high-genus meshes, , chairs and backpacks.
